Suicide Risk Prediction from Patient Interviews
The Pestian Lab group has conducted two prospective studies to understand the acoustic, linguistic, facial expression, and genetic features of suicidal patients and patients with mental illness. The goal of the first study, the Suicidal Adolescent Clinical Trial (ACT) conducted in 2011 was to build a machine learning classifier to differentiate suicidal and control adolescent patients using their linguistic and vocal characteristics. The ongoing STM (Suicide Thought Marker) study is an expanded version of the ACT study, where the main goal is to build a machine learning classifier to differentiate suicidal patients, patients with mental illness, and control patients using their linguistic, vocal, genetic and/or facial expression characteristics.
In both studies, machine learning techniques have been used to determine suicidal risk using linguistic and acoustic features. Ongoing analyses of the STM will determine the power of facial expression and genetic features in complementing the assessment of suicidal risk and mental illness.
Machine learning classifiers were built using either linguistic features or acoustic features from the ACT data set. These classifiers were able to differentiate between suicidal and control patients with at least 90% accuracy. Publications related to the STM data are in progress, but comparable accuracies are expected; although the STM cohort is more diverse in age range, demographics, and variety of mental disorders, the cohort is larger.
